{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python绘制热力图查看二分类变量的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 热力图(heat map)\n",
    "\n",
    "热力图（或者色块图），由小色块组成的二维图表，其中：\n",
    "* x、y轴可以是分类变量，对应的小方块由连续数值表示颜色强度\n",
    "* 即用两个分类字段确定数值点的位置，用于展示数据的分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "sns.set_style('whitegrid',{'font.sans-serif':['simhei','Arial']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实例1：模拟绘制北京景区热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>周一</th>\n",
       "      <th>周二</th>\n",
       "      <th>周三</th>\n",
       "      <th>周四</th>\n",
       "      <th>周五</th>\n",
       "      <th>周六</th>\n",
       "      <th>周日</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>天安门</th>\n",
       "      <td>0.089748</td>\n",
       "      <td>0.779652</td>\n",
       "      <td>0.863827</td>\n",
       "      <td>0.411872</td>\n",
       "      <td>0.985436</td>\n",
       "      <td>0.400011</td>\n",
       "      <td>0.473167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>故宫</th>\n",
       "      <td>0.675329</td>\n",
       "      <td>0.691779</td>\n",
       "      <td>0.288613</td>\n",
       "      <td>0.581386</td>\n",
       "      <td>0.568672</td>\n",
       "      <td>0.459483</td>\n",
       "      <td>0.215607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>奥林匹克森林公园</th>\n",
       "      <td>0.251856</td>\n",
       "      <td>0.502402</td>\n",
       "      <td>0.735914</td>\n",
       "      <td>0.476470</td>\n",
       "      <td>0.601322</td>\n",
       "      <td>0.803051</td>\n",
       "      <td>0.445285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>八达岭长城</th>\n",
       "      <td>0.975519</td>\n",
       "      <td>0.656879</td>\n",
       "      <td>0.760616</td>\n",
       "      <td>0.517780</td>\n",
       "      <td>0.961195</td>\n",
       "      <td>0.712428</td>\n",
       "      <td>0.135489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                周一        周二        周三        周四        周五        周六        周日\n",
       "天安门       0.089748  0.779652  0.863827  0.411872  0.985436  0.400011  0.473167\n",
       "故宫        0.675329  0.691779  0.288613  0.581386  0.568672  0.459483  0.215607\n",
       "奥林匹克森林公园  0.251856  0.502402  0.735914  0.476470  0.601322  0.803051  0.445285\n",
       "八达岭长城     0.975519  0.656879  0.760616  0.517780  0.961195  0.712428  0.135489"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    np.random.rand(4, 7), \n",
    "    columns = [\"周一\", \"周二\", \"周三\", \"周四\", \"周五\", \"周六\", \"周日\"],\n",
    "    index = [\"天安门\", \"故宫\", \"奥林匹克森林公园\", \"八达岭长城\"]\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2040b125a08>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 8))\n",
    "sns.heatmap(df, annot=True, fmt = \".2f\", cmap = \"coolwarm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实例2：绘制泰坦尼克事件与存亡变量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取并合并泰坦尼克数据\n",
    "df = pd.concat(\n",
    "    [\n",
    "        pd.read_csv(\"./datas/titanic/titanic_train.csv\"),\n",
    "        pd.read_csv(\"./datas/titanic/titanic_test.csv\")\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1       0.0       3   \n",
       "1            2       1.0       1   \n",
       "2            3       1.0       3   \n",
       "3            4       1.0       1   \n",
       "4            5       0.0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  1309 non-null   int64  \n",
      " 1   Survived     891 non-null    float64\n",
      " 2   Pclass       1309 non-null   int64  \n",
      " 3   Name         1309 non-null   object \n",
      " 4   Sex          1309 non-null   object \n",
      " 5   Age          1046 non-null   float64\n",
      " 6   SibSp        1309 non-null   int64  \n",
      " 7   Parch        1309 non-null   int64  \n",
      " 8   Ticket       1309 non-null   object \n",
      " 9   Fare         1308 non-null   float64\n",
      " 10  Cabin        295 non-null    object \n",
      " 11  Embarked     1307 non-null   object \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 132.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把字符串类型的列，变成分类数字编码\n",
    "for field in [\"Sex\", \"Cabin\", \"Embarked\"]:\n",
    "    df[field] = df[field].astype(\"category\").cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  1309 non-null   int64  \n",
      " 1   Survived     891 non-null    float64\n",
      " 2   Pclass       1309 non-null   int64  \n",
      " 3   Name         1309 non-null   object \n",
      " 4   Sex          1309 non-null   int8   \n",
      " 5   Age          1046 non-null   float64\n",
      " 6   SibSp        1309 non-null   int64  \n",
      " 7   Parch        1309 non-null   int64  \n",
      " 8   Ticket       1309 non-null   object \n",
      " 9   Fare         1308 non-null   float64\n",
      " 10  Cabin        1309 non-null   int16  \n",
      " 11  Embarked     1309 non-null   int8   \n",
      "dtypes: float64(3), int16(1), int64(4), int8(2), object(2)\n",
      "memory usage: 107.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1       0.0       3   \n",
       "1            2       1.0       1   \n",
       "2            3       1.0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
       "\n",
       "             Ticket     Fare  Cabin  Embarked  \n",
       "0         A/5 21171   7.2500     -1         2  \n",
       "1          PC 17599  71.2833    106         0  \n",
       "2  STON/O2. 3101282   7.9250     -1         2  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
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       "      <th>Fare</th>\n",
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       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.038354</td>\n",
       "      <td>0.013406</td>\n",
       "      <td>0.028814</td>\n",
       "      <td>-0.055224</td>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.031428</td>\n",
       "      <td>-0.012096</td>\n",
       "      <td>-0.048530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.543351</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>0.277885</td>\n",
       "      <td>-0.176509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.038354</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.124617</td>\n",
       "      <td>-0.408106</td>\n",
       "      <td>0.060832</td>\n",
       "      <td>0.018322</td>\n",
       "      <td>-0.558629</td>\n",
       "      <td>-0.534483</td>\n",
       "      <td>0.192867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0.013406</td>\n",
       "      <td>-0.543351</td>\n",
       "      <td>0.124617</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.063645</td>\n",
       "      <td>-0.109609</td>\n",
       "      <td>-0.213125</td>\n",
       "      <td>-0.185523</td>\n",
       "      <td>-0.126367</td>\n",
       "      <td>0.104818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.028814</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.408106</td>\n",
       "      <td>0.063645</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.243699</td>\n",
       "      <td>-0.150917</td>\n",
       "      <td>0.178740</td>\n",
       "      <td>0.190984</td>\n",
       "      <td>-0.089292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.055224</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.060832</td>\n",
       "      <td>-0.109609</td>\n",
       "      <td>-0.243699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.373587</td>\n",
       "      <td>0.160238</td>\n",
       "      <td>-0.005685</td>\n",
       "      <td>0.067802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.008942</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018322</td>\n",
       "      <td>-0.213125</td>\n",
       "      <td>-0.150917</td>\n",
       "      <td>0.373587</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.221539</td>\n",
       "      <td>0.029582</td>\n",
       "      <td>0.046957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.031428</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.558629</td>\n",
       "      <td>-0.185523</td>\n",
       "      <td>0.178740</td>\n",
       "      <td>0.160238</td>\n",
       "      <td>0.221539</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.340217</td>\n",
       "      <td>-0.241442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>-0.012096</td>\n",
       "      <td>0.277885</td>\n",
       "      <td>-0.534483</td>\n",
       "      <td>-0.126367</td>\n",
       "      <td>0.190984</td>\n",
       "      <td>-0.005685</td>\n",
       "      <td>0.029582</td>\n",
       "      <td>0.340217</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.126482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>-0.048530</td>\n",
       "      <td>-0.176509</td>\n",
       "      <td>0.192867</td>\n",
       "      <td>0.104818</td>\n",
       "      <td>-0.089292</td>\n",
       "      <td>0.067802</td>\n",
       "      <td>0.046957</td>\n",
       "      <td>-0.241442</td>\n",
       "      <td>-0.126482</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Sex       Age     SibSp  \\\n",
       "PassengerId     1.000000 -0.005007 -0.038354  0.013406  0.028814 -0.055224   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.543351 -0.077221 -0.035322   \n",
       "Pclass         -0.038354 -0.338481  1.000000  0.124617 -0.408106  0.060832   \n",
       "Sex             0.013406 -0.543351  0.124617  1.000000  0.063645 -0.109609   \n",
       "Age             0.028814 -0.077221 -0.408106  0.063645  1.000000 -0.243699   \n",
       "SibSp          -0.055224 -0.035322  0.060832 -0.109609 -0.243699  1.000000   \n",
       "Parch           0.008942  0.081629  0.018322 -0.213125 -0.150917  0.373587   \n",
       "Fare            0.031428  0.257307 -0.558629 -0.185523  0.178740  0.160238   \n",
       "Cabin          -0.012096  0.277885 -0.534483 -0.126367  0.190984 -0.005685   \n",
       "Embarked       -0.048530 -0.176509  0.192867  0.104818 -0.089292  0.067802   \n",
       "\n",
       "                Parch      Fare     Cabin  Embarked  \n",
       "PassengerId  0.008942  0.031428 -0.012096 -0.048530  \n",
       "Survived     0.081629  0.257307  0.277885 -0.176509  \n",
       "Pclass       0.018322 -0.558629 -0.534483  0.192867  \n",
       "Sex         -0.213125 -0.185523 -0.126367  0.104818  \n",
       "Age         -0.150917  0.178740  0.190984 -0.089292  \n",
       "SibSp        0.373587  0.160238 -0.005685  0.067802  \n",
       "Parch        1.000000  0.221539  0.029582  0.046957  \n",
       "Fare         0.221539  1.000000  0.340217 -0.241442  \n",
       "Cabin        0.029582  0.340217  1.000000 -0.126482  \n",
       "Embarked     0.046957 -0.241442 -0.126482  1.000000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算不同变量之间两两相关系数\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2040b851048>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "sns.heatmap(df.corr(), annot=True, fmt = \".2f\", cmap = \"coolwarm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
